1. Field of the Invention
The invention relates to a process of multisensor equalization allowing the demodulation of a digital message of serial digital modulation type in the presence of multiple propagation paths and interfering sources, also called jammers in respect of modulations formed from frames comprising learning sequences and information symbol sequences. It is based on the techniques of antenna processing and therefore requires the use of an array containing several sensors.
2. Discussion of the Background
For numerous applications in digital radio communications, transmission between the transmitter and the receiver occurs along several propagation paths. Since the delay time between the various paths may be greater than the symbol duration, equalization becomes necessary in order to compensate for the inter-symbol interference (ISI) thus generated.
This phenomenon occurs in particular in the HF range, where the multiple propagation paths arising from the reflections off the various ionospheric layers, may be 5 ms apart, i.e. several times the symbol duration (for modulations whose bandwidth is typically of the order of 3 kHz). It also occurs in other frequency ranges in respect of very high speed communications, of the GSM type (270 kbits/s, i.e. a symbol duration of 3.7 .mu.s), in, an urban or mountainous setting, where the various paths stemming from reflections off various obstacles (buildings, mountains, etc.) may be separated by 10 or even 20 .mu.s.
The invention may therefore be applied in particular to the high-frequency (HF) range which is of particular interest to radio communications since it allows long-distance communications on account of the phenomena of reflection off the various layers of the ionosphere, or else in respect of GSM-type applications. The invention may also lead to an increase in the capacity of cellular radio communication systems by allowing the implementation of Space Division Multiple Access (SDMA) techniques which consist in allowing several users who are sufficiently far apart spatially to use the same frequency at the same time.
In many systems currently in service, adaptation to the conditions of propagation is made possible by inserting learning sequences, which are known to the receiver, into the waveform. Various solutions are then possible for carrying out adaptive equalization of the useful signal received, the two most common being:
equalization by a Viterbi algorithm, requiring prior estimation of the propagation channel using the learning sequence. This equalization has the advantage of minimizing the probability of error with regard to the complete sequence of information signals, but it becomes very expensive when the duration of the impulse response of the channel is much greater than the symbol duration Ts. This is because the number of states which the Viterbi algorithm must process is equal to M.sup.L, where M is the size of the alphabet of the modulation and L the length of the impulse response of the channel in terms of number of symbol periods. This solution is used for GSM-type applications where the Viterbi algorithm typically contains 32 states (L=5 and M=2). PA1 the sensors are identical and arranged at various points in space, discrimination between the useful signal and the jammers being effected via the direction of arrival, PA1 the sensors are arranged at one point in space (co-localized antenna) and possess different radiation patterns. Discrimination may then be effected according to polarization and direction of arrival, PA1 the above two possibilities can be combined: several co-localized antennas may be arranged at various points in space. PA1 based on direction of arrival, PA1 based on modulation, PA1 based on time, for example, for frequency-hopping links, PA1 based on power PA1 blindly (for example, the higher-order source separation methods). PA1 "points" in the direction of one of the paths (the one which is correlated with the replica), that is to say puts the contributions from this path back into phase on the various sensors (for omnidirectional sensors a gain is therefore obtained in the signal-to-noise ratio of 10 log N, where N is the number of sensors used), PA1 and attempts to reject the uncorrelated paths thereof (thus losing the energy associated with these paths), the latter being viewed by the antenna as completely separate jammers. Such an antenna can therefore be greatly impaired in the presence of several useful propagation paths. This is because the uncorrelated useful paths may be rejected to the detriment of the rejection of the jammers, and the performance of the multisensor receiver may even become poorer than that of the single-sensor receiver when two temporally uncorrelated propagation paths are highly correlated spatially. PA1 a first step consisting in digitizing the signal received by each sensor, in transforming the digitized signal to baseband, and in filtering the baseband signal by a low-pass filtering, PA1 a second step consisting in performing a seizure of synchronization on the signals emanating from the first step, in estimating the useful paths and the frequency shift resulting from inaccuracies in the transmission and reception frequencies and from ionospheric propagation, PA1 a third step consisting in compensating for the shift in frequency of the signals delivered by each sensor, on the basis of the estimation performed in the second step, PA1 a fourth step consisting in calculating on the one hand the coefficients of a spatial filter applied to the signals emanating from the third step and on the other hand the coefficients of a temporal filter applied to the replica signal, made up either of the known symbols for the learning sequences, or from the demodulated symbols for the information sequences, the coefficients of these two filters being calculated so as to minimize, under a specified constraint, a criterion of mean square error between the output signal from the spatial filter and the output signal from the temporal filter, and consisting in filtering the signals emanating from the third step with the aid of the thus calculated coefficients of the spatial filter, thus optimizing the gain of the array of sensors in the direction of the useful signal at the output of the spatial filtering and ensuring rejection of the interference, and PA1 a fifth step consisting in equalizing the signal emanating from the fourth step by one-dimensional equalization at a symbol rate deciding the symbols transmitted; PA1 the coefficients of the spatial and equalizing filters as well as the estimate of the useful channel being updated according to a specified sequencing of the frames.
In the HF range, the number of states becomes too large for the Viterbi algorithm to be usable (typically, M is equal to 4 or 8, and L is equal to 12, corresponding to an impulse response stretching over 5 ms) and the second solution using a DFE equalizer, standing for "Decision Feedback Equalizer", is often used.
This second solution consists in using the learning sequences to optimize a MSE (Mean Square Error) criterion. The equalizer attempts to provide the decision facility, adapted to the modulation, with a signal devoid of ISI, or in which the ISI has been greatly reduced. For this purpose, the DFE equalizer uses transverse filters and auto-adaptive recursive filters, which are adapted by an algorithm of the recursive least squares type (preferably to a gradient algorithm for reasons of speed of convergence) or are calculated directly from an estimate of the transmission channel--see in this respect the article by P. K. Shukla and L. F. Turner, "Channel-estimation-based adaptive DFE for fading multipath radio channels", Proc. of 1989 International Conference on Communications, ICC'89[1]. In the learning sequences, the known symbols are used to adapt the various coefficients. The tracking of the channel variations outside of the known sequences is ensured by using the symbols as and when decided as replica.
In the HF range, the various propagation paths are usually affected by flat "fading". When this "fading" is large, the performance of the DFE equalizer is degraded.
On the other hand, when jamming is present, these techniques rapidly become ineffective and special anti-jamming techniques are necessary, such as error-correcting coding, the removal of jamming by notch filtering or the use of frequency-hopping links. These techniques, used in numerous operational systems, are nevertheless limited when the interference is strong and occupies the whole of the useful signal band. Under these conditions, higher-performance anti-jamming means should be used, based on the use of antenna filtering techniques.
Antenna filtering techniques, which appeared in the early 1960s and are described in particular in an article by P. W. Howells, "Explorations in fixed and adaptative resolution at GE and SURC", IEEE Trans-Ant-Prop, Vol. AP-24, No. 5, pp 575-584, September 1976 [2], an exhaustive overview of which is presented in a thesis by P. Chevalier, "Antenne adaptative: d'une structure lineaire a une structure non lineaire de Voltera [Adaptative antenna: from a linear structure to a nonlinear Voltera structure]", June 1991 [3], aim to combine the signals received by the various component sensors of the antenna, in such a way as to optimize the response of the latter to the useful-signal and jammers scenario.
The choice of sensors and of their arrangement is an important parameter and has a major influence on performance. Three types of possibilities may be envisaged:
However, since the propagation and jamming conditions may alter over time, it is necessary to be able to adapt the antenna in real time to these variations through the use of a particular antenna filtering technique: the adaptative antenna. An adaptative antenna is an antenna which detects the sources of interference automatically, while constructing holes in its radiation pattern in their direction, while simultaneously improving the reception of the useful source, without a priori knowledge about the interference and on the basis of minimum information about the useful signal. Moreover, on account of the tracking ability of the algorithms used, an adaptative antenna is capable of responding automatically to a changing environment.
Adaptative antennas are characterized by the way in which they discriminate between the useful signal and the jammers, that is to say by the nature of the information about the useful signal which they exploit. This discrimination can be effected in five different ways according to [3]:
Until very recently, transmission systems were still envisaged as operating independently of the adaptative antenna and single-sensor adaptative equalization techniques, this leading to sub-optimal performance.
Thus, the Dobson system described in U.S. Pat. No. PCT/AU85/00157 by R. Dobson entitled "Adaptative antenna array", February 1986 [4], which uses time-based discrimination, succeeds in effectively rejecting the jammers but does not seek to optimize the useful signal/background noise ratio. Moreover, it can only be used when the waveform allows reception when no useful signal is present.
Within a transmission context, and when learning sequences are introduced into the waveform, it is preferable to use antenna processing techniques with modulation-based discrimination since these make it possible to optimize the useful signal/noise ratio, while avoiding the implementation of a direction-finding step. However, those which are employed nowadays use complex weights in respect of each of the sensors of the adaptative antenna which are adapted via a criterion of minimization of an MSE between the output signal of the antenna and a replica signal. Such an antenna, known as an SAFR (Spatial Adapted Filter adapted with the aid of a Replica), allows the rejection of jammers, but in the presence of multiple propagation paths, it:
To improve the performance of this latter antenna processing technique, the idea is to couple it with a single-sensor equalization technique. Multisensor equalizers are thus obtained which comprise a spatial part, composed of various filters arranged on each of the reception pathways, and a temporal part arranged at the output of the spatial part.
Several multisensor equalizers of this type have already been proposed and studied, essentially within the field of mobile radio transmissions. See in this regard the articles by K. E. Scott and S. T. Nichols, "Antenna Diversity with Multichannel Adaptative Equalization in Digital Radio" [5] and P. Balaban and J. Salz, "Optimum Diversity Combining and Equalization in Digital Data Transmission with Applications to Cellular Mobile Radio--Part 1: Theoretical Considerations", IEEE Trans. on Com., Vol. 40, No. 5, pp 885-894, May 1992 [6]. They have up until now been envisaged for combating the selective "fading" created by the multipaths, in an unjammed environment. These equalizers consist of Finite Impulse Response filters, one on each of the pathways, followed by an adder, and then by a symbol-rate one-dimensional equalizer. The criterion used to optimize these multisensor equalizers is that of minimizing the MSE between their output and a replica.
In the equalizer proposed by Scott et al. [5], the adaptation of the coefficients is performed by a least squares algorithm, and its use in respect of an HF channel cannot be envisaged for the waveforms used, since, if the temporal spreading of the multipaths is taken into account, the number of coefficients to be adapted is too large for the algorithm to be able to converge on the learning sequence.
In the equalizer proposed by Balaban et al. [6], the coefficients are calculated after estimating the propagation channel. The article does not tackle the problem of a jammed environment.
A multisensor equalizer which leads to an improvement in the performance of existing multisensor equalizers, in particular by allowing anti-jamming, has formed the subject of a patent application filed in France by the Applicant on Feb. 25, 1994, entitled "Procede permettant une egalisation multivoies dans un recepteur radioelectrique, en presence d'interferences et de multitrajets de propagation [Process allowing multichannel equalization in a radio receiver, in the presence of interference and multiple propagation paths]" [7], and published as No. 2 716 761. Spatial-diversity equalizers based on an estimate of the transmission channel and operating in an unjammed environment can be made robust by this equalizer by incorporating a jammer rejection function (performed by reprocessing sensor signals) therein. This equalizer is of special interest since it is optimal when the noise is temporally white (temporally white jammer(s) and background noise and jammer(s) possessing a single propagation path), irrespective of the number of paths associated with the useful signal. On the other hand, its implementation requires a computational power which may become large when the length of the impulse response of the useful propagation channel increases, and this may become injurious for certain applications.